Cost Savings from Integrating Behavioral Health in Primary Care: A Pragmatic Randomized Control Trial with Karen Refugees
Bibliographic record
Abstract
Many refugees experience exposure to chronic and traumatic stressors that can lead to complex mental health and other health care needs. The integration of behavioral health into primary care is a promising approach for addressing complex health needs; however, it has been understudied with refugee and immigrant populations. Using a pragmatic randomized control trial design, this study examined inpatient and outpatient health service utilization and associated costs of a primary care-based intensive psychotherapy and case management intervention for 214 Karen refugees with major depression compared to care as usual over time. Results indicated the addition of the behavioral health intervention was associated with reduced inpatient healthcare costs vs. care as usual, shorter hospital stays, and improved patient status at discharge. The average inpatient cost saving exceeded $8,000 per patient among the intervention group. After controlling for key patient characteristics, patients who received the intervention accrued lower outpatient costs as compared to care as usual over 18 months. Findings suggested the integrated behavioral health intervention resulted in lower healthcare costs among refugees with complex health needs engaged in primary health care. Future research is needed to better understand long-term effects and further optimize care for refugees.Trial Registration clinicaltrials.gov Identifier NCT03788408. Registered 20 Dec 2018. Retrospectively registered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".